Crowd‐sourced plant occurrence data provide a reliable description of macroecological gradients

Abstract

Deep learning algorithms classify plant species with high accuracy, and smartphone applications leverage this technology to enable users to identify plant species in the field. The question we address here is whether such crowd‐sourced data contain substantial macroecological information. In particular, we aim to understand if we can detect known environmental gradients shaping plant co‐occurrences. In this study we analysed 1 million data points collected through the use of the mobile app Flora Incognita between 2018 and 2019 in Germany and compared them with Florkart, containing plant occurrence data collected by more than 5000 floristic experts over a 70‐year period. The direct comparison of the two data sets reveals that the crowd‐sourced data particularly undersample areas of low population density. However, using nonlinear dimensionality reduction we were able to uncover macroecological patterns in both data sets that correspond well to each other. Mean annual temperature, temperature seasonality and wind dynamics as well as soil water content and soil texture represent the most important gradients shaping species composition in both data collections. Our analysis describes one way of how automated species identification could soon enable near real‐time monitoring of macroecological patterns and their changes, but also discusses biases that must be carefully considered before crowd‐sourced biodiversity data can effectively guide conservation measures.

Applicability of Point- and Polygon-Based Vegetation Monitoring Data to Identify Soil, Hydrological and Climatic Driving Forces of Biological Invasions—A Case Study of Ailanthus altissima, Elaeagnus angustifolia and Robinia pseudoacacia

“…Several studies note that the main weakness of crowd-sourced, geotagged, photo-based vegetation maps is that the spatial density of field data is very diverse, that is, very spatially fragmented data [ 38 , 39 , 40 ]. However, the LUCAS database is based on spatially uniform survey points with uniform data density, and therefore, the representativeness of the data is higher compared to crowd-sourced vegetation databases.…”

Citizen science: How to extend reciprocal benefits from the project community to the broader socio-ecological system

“…Moreover, from a research point of view, collected plant-related data may be used with other datasets, such as meteorological and climate data, increasing the power of the collected data. A recent study showed that from crowd-sourced flower identification data, it was possible to rebuild spatial macroecological gradients (Mahecha et al, 2021 ). This means that we can potentially extract more information than the app was initially designed to deliver.…”

The Sustainability Argument for Open Science

“…crowd-sourcing research questions (e.g., CRIS); citizens engaging in data collection directly (examples from climate-related research include CurieuzeNeuzen for measuring urban nitrogen dioxide levels, INCREASE for increasing pulse biodiversity, bird counting initiatives to investigate how environmental changes affect birds in North America and Germany, and Flora Incognita for mapping plant occurrences (Mahecha et al, 2021)); and data analysis (e.g.…”